Data Visualization

1 Data Source:

linked

2 Data Set Information:

The data set consists of the expression levels of 77 proteins/protein modifications that produced detectable signals in the nuclear fraction of cortex. There are 38 control mice and 34 trisomic mice (Down syndrome), for a total of 72 mice. In the experiments, 15 measurements were registered of each protein per sample/mouse. Therefore, for control mice, there are 38x15, or 570 measurements, and for trisomic mice, there are 34x15, or 510 measurements. The dataset contains a total of 1080 measurements per protein. Each measurement can be considered as an independent sample/mouse.

The eight classes of mice are described based on features such as genotype, behavior and treatment. According to genotype, mice can be control or trisomic. According to behavior, some mice have been stimulated to learn (context-shock) and others have not (shock-context) and in order to assess the effect of the drug memantine in recovering the ability to learn in trisomic mice, some mice have been injected with the drug and others have not.

Classes:

• c-CS-s: control mice, stimulated to learn, injected with saline (9 mice)
• c-CS-m: control mice, stimulated to learn, injected with memantine (10 mice)
• c-SC-s: control mice, not stimulated to learn, injected with saline (9 mice)

• c-SC-m: control mice, not stimulated to learn, injected with memantine (10 mice)

• t-CS-s: trisomy mice, stimulated to learn, injected with saline (7 mice)
• t-CS-m: trisomy mice, stimulated to learn, injected with memantine (9 mice)
• t-SC-s: trisomy mice, not stimulated to learn, injected with saline (9 mice)

• t-SC-m: trisomy mice, not stimulated to learn, injected with memantine (9 mice)

3 Attribute Information:

1. Mouse ID
2. Values of expression levels of 77 proteins; indicating that they were measured in the nuclear fraction. For example: DYRK1A_n
3. Genotype: control (c) or trisomy (t)
4. Treatment type: memantine (m) or saline (s)
5. Behavior: context-shock (CS) or shock-context (SC)
6. Class: c-CS-s, c-CS-m, c-SC-s, c-SC-m, t-CS-s, t-CS-m, t-SC-s, t-SC-m

library(readxl)
data <- read_excel("Data_Cortex_Nuclear.xlsx")
head(data)

## # A tibble: 6 x 82
##   MouseID class..2 ITSN1_N BDNF_N NR1_N NR2A_N pAKT_N pBRAF_N pCAMKII_N
##   <chr>   <chr>      <dbl>  <dbl> <dbl>  <dbl>  <dbl>   <dbl>     <dbl>
## 1 309_1   309        0.747  0.430  2.82   5.99  0.219   0.178      2.37
## 2 309_2   309        0.689  0.412  2.79   5.69  0.212   0.173      2.29
## 3 309_3   309        0.730  0.418  2.69   5.62  0.209   0.176      2.28
## 4 309_4   309        0.617  0.359  2.47   4.98  0.223   0.176      2.15
## 5 309_5   309        0.617  0.359  2.37   4.72  0.213   0.174      2.13
## 6 309_6   309        0.628  0.367  2.39   4.81  0.219   0.176      2.14
## # ... with 73 more variables: pCREB_N <dbl>, pELK_N <dbl>, pERK_N <dbl>,
## #   pJNK_N <dbl>, PKCA_N <dbl>, pMEK_N <dbl>, pNR1_N <dbl>, pNR2A_N <dbl>,
## #   pNR2B_N <dbl>, pPKCAB_N <dbl>, pRSK_N <dbl>, AKT_N <dbl>,
## #   BRAF_N <dbl>, CAMKII_N <dbl>, CREB_N <dbl>, ELK_N <dbl>, ERK_N <dbl>,
## #   GSK3B_N <dbl>, JNK_N <dbl>, MEK_N <dbl>, TRKA_N <dbl>, RSK_N <dbl>,
## #   APP_N <dbl>, Bcatenin_N <dbl>, SOD1_N <dbl>, MTOR_N <dbl>,
## #   P38_N <dbl>, pMTOR_N <dbl>, DSCR1_N <dbl>, AMPKA_N <dbl>,
## #   NR2B_N <dbl>, pNUMB_N <dbl>, RAPTOR_N <dbl>, TIAM1_N <dbl>,
## #   pP70S6_N <dbl>, NUMB_N <dbl>, P70S6_N <dbl>, pGSK3B_N <dbl>,
## #   pPKCG_N <dbl>, CDK5_N <dbl>, S6_N <dbl>, ADARB1_N <dbl>,
## #   AcetylH3K9_N <dbl>, RRP1_N <dbl>, BAX_N <dbl>, ARC_N <dbl>,
## #   ERBB4_N <dbl>, nNOS_N <dbl>, Tau_N <dbl>, GFAP_N <dbl>, GluR3_N <dbl>,
## #   GluR4_N <dbl>, IL1B_N <dbl>, P3525_N <dbl>, pCASP9_N <dbl>,
## #   PSD95_N <dbl>, SNCA_N <dbl>, Ubiquitin_N <dbl>, pGSK3B_Tyr216_N <dbl>,
## #   SHH_N <dbl>, BAD_N <dbl>, BCL2_N <dbl>, pS6_N <dbl>, pCFOS_N <dbl>,
## #   SYP_N <dbl>, H3AcK18_N <dbl>, EGR1_N <dbl>, H3MeK4_N <dbl>,
## #   CaNA_N <dbl>, Genotype <chr>, Treatment <chr>, Behavior <chr>,
## #   class..82 <chr>

3.1 Scatter plot

#Categorical independent variable
library(gridExtra)
library(ggplot2)  
a<- ggplot(data, aes(x=Treatment,y=ITSN1_N))+
  geom_point(aes(color=Behavior))+
  facet_wrap(~Genotype)+
  scale_fill_brewer() +
  theme(legend.position = c(0.85, 0.8))

#Continuous variables
b<- ggplot(data, aes(BDNF_N, ITSN1_N)) + 
  geom_point(aes(color = Behavior)) + 
  theme_bw()+
  labs(title="Scatterplot")+
  theme(legend.position = c(0.2, 0.8))

grid.arrange(a,b, ncol=2,nrow=1)

ggplot(data, aes(ELK_N, ERK_N)) + 
  geom_point(aes(color = class..82)) + 
  theme_bw()+
  scale_colour_brewer(palette = "Set2")+
  facet_wrap(~class..82)

3.2 Correlation Matrix - first 10 protein

library(corrplot)
data1 <- data[,c(3:12)]
data1 <- na.omit(data1)
names(data1)

##  [1] "ITSN1_N"   "BDNF_N"    "NR1_N"     "NR2A_N"    "pAKT_N"   
##  [6] "pBRAF_N"   "pCAMKII_N" "pCREB_N"   "pELK_N"    "pERK_N"

M1 <- cor(data1)
cor.mtest <- function(mat, ...) {
    mat <- as.matrix(mat)
    n <- ncol(mat)
    p.mat<- matrix(NA, n, n)
    diag(p.mat) <- 0
    for (i in 1:(n - 1)) {
        for (j in (i + 1):n) {
            tmp <- cor.test(mat[, i], mat[, j], ...)
            p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
        }
    }
  colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
  p.mat
}
# matrix of the p-value of the correlation
p.mat <- cor.mtest(mtcars)
corrplot(M1,  type="upper", order="hclust",tl.col="black", tl.srt=45, p.mat = p.mat, sig.level = 0.01)

corrplot(M1, method="color",  
         type="upper", order="hclust", 
         addCoef.col = "black", 
         tl.col="black", tl.srt=45,
         p.mat = p.mat, sig.level = 0.01, insig = "blank", 
         diag=FALSE 
         )

plot(data1)

3.3 Correlogram

library(corrgram)

## Warning: package 'corrgram' was built under R version 3.5.3

data1 <- data[,60:82]
corrgram(data1, order=NULL, panel=panel.shade, text.panel=panel.txt,
           main="Correlogram") 

3.4 Boxplot

library(reshape2)
library(ggplot2)
library(plotly)
library(ggthemes) #custom theme background
#using ggplot
g <- ggplot(data, aes(class..82,ITSN1_N,fill=class..82)) 
g +  geom_boxplot()+
  scale_y_continuous("ITSN1_N", breaks= seq(0,15000, by=500))+
  labs(title="Box plot", 
       subtitle="ITSN1_N gene grouped by Treatment",
       caption="Source: mpg",
       x="Treatment",
       y="ITSN1_N") +
  guides(fill=guide_legend("Treatment"))+ 
  theme_stata() + #background theme
  scale_color_stata() + #background theme
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) 

#plot_ly
data2 <- data[,c(70:77,79,80,81,82)]
df <- melt(data2,id=c("Genotype","Behavior","class..82","Treatment" ))
plot_ly(df, x = ~variable, y = ~value, color = ~Genotype,type = "box", colors = "Set3")%>% 
         layout(boxmode = "group",
                xaxis = list(title=''), 
                yaxis = list(title='Value'))

3.5 Violin plot

#violin
g+ geom_violin(aes(fill=factor(Behavior))) +
  labs(title="Violin plot") +
  guides(fill=guide_legend("Behavior"))+ 
   theme_economist() + 
  scale_color_economist()+
  theme(legend.background = element_rect(fill="lightblue",
                                  size=0.5, linetype="solid", 
                                  colour ="darkblue"))

## Warning: Removed 3 rows containing non-finite values (stat_ydensity).

3.6 Dendrogram plot

library(dplyr) #for dataset manipulation
library(knitr) #for neaty dataset printing
library(car)
data2 <- na.omit(data)
dfdf1<-data %>% group_by(class..82) %>%
 summarise_at(vars(pMTOR_N:pGSK3B_N),mean, na.rm=TRUE)
dfdf2<-data %>% group_by(class..82) %>%
 summarise_at(vars(pMTOR_N:pGSK3B_N),mean, na.rm=TRUE) %>%
 filter(class..82 %in% c("c-CS-m"))
dfdf2t <- t(dfdf2)
names <- rownames(dfdf2t)
dfdf3 <- cbind(names,dfdf2t)
dfdf3 <- dfdf3[-1,]
rownames(dfdf3) <- NULL
colnames(dfdf3) <- c("protein","mean")
kable(dfdf3)

protein	mean
pMTOR_N	0.7610186
DSCR1_N	0.5952344
AMPKA_N	0.3940157
NR2B_N	0.5934115
pNUMB_N	0.4108883
RAPTOR_N	0.3276684
TIAM1_N	0.4213292
pP70S6_N	0.3228659
NUMB_N	0.1844747
P70S6_N	0.9298689
pGSK3B_N	0.166257

means <- dfdf3[,2]
d_mean <- dist(means, method = "euclidean")
fit <- hclust(d_mean)
h <- 2
groups <- cutree(fit, h = h)
plot(fit, labels = dfdf3[,1], main = "Dendrogram")

3.7 Chord-diagrams

library(circlize) #for chord-diagrams
dfdf11<-data %>% group_by(class..82) %>%
 summarise_at(vars(APP_N:MTOR_N),mean, na.rm=TRUE)
dfdf11 <- as.data.frame(dfdf11)
rownames(dfdf11) <- dfdf11[,1]
dfdf11 <- dfdf11[,-1]
chordDiagram(dfdf11)

3.8 Radar Plot

par(mfrow=c(1,2))
#Empty inside
library(fmsb)
dfdf22<-data %>% group_by(class..82) %>%
 summarise_at(vars(pMTOR_N:pGSK3B_N),mean, na.rm=TRUE) %>%
 filter(class..82 %in% c("t-CS-s"))
dfdf22<- dfdf22[,-1]

dfdf23<-data %>% group_by(class..82) %>%
 summarise_at(vars(pMTOR_N:pGSK3B_N),mean, na.rm=TRUE) %>%
 filter(class..82 %in% c("t-CS-m"))
dfdf23<- dfdf23[,-1]
maxdf <- rep(1,12)
mindf <- rep(0,12)
dfdf3<- data.frame(rbind(maxdf,mindf,dfdf22,dfdf23))
radarchart(dfdf3)


#Fill in
dfdf24<-data %>% group_by(class..82) %>%
 summarise_at(vars(pMTOR_N:pGSK3B_N),mean, na.rm=TRUE) 
dfdf24 <- data.frame(t(dfdf24))
#firt row as column name
rownames(dfdf24[1,]) <- NULL
colnames(dfdf24) <- as.character(unlist(dfdf24[1,])) 
dfdf24 = dfdf24[-1, ]
#factor to numeric - all variables
con.names = dfdf24 %>% select_if(is.factor) %>% colnames()
dfdf24[,con.names] = lapply(dfdf24[,con.names], as.character)
can.names = dfdf24 %>% select_if(is.character) %>% colnames()
dfdf24[,can.names] = lapply(dfdf24[,can.names], as.numeric)


dfdf1 <- dfdf24[1,]
dfdf2 <- dfdf24[2,]
dfdf3 <- dfdf24[3,]
comparedf <- rbind(maxdf,mindf,dfdf1,dfdf2,dfdf3)
radarchart(comparedf)

colors_border=c( rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) , rgb(0.7,0.5,0.1,0.9) )
colors_in=c( rgb(0.2,0.5,0.5,0.4), rgb(0.8,0.2,0.5,0.4) , rgb(0.7,0.5,0.1,0.4) )
radarchart( comparedf  , axistype=1 , 
    #custom polygon
    pcol=colors_border, pfcol=colors_in, plwd=2 , plty=1,
    #custom the grid
    cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,20,5), cglwd=0.8,
    #custom labels
    vlcex=0.8 
    )
legend(x=1.5, y=1, legend = rownames(comparedf[-c(1,2),]), bty = "n", pch=20 , col=colors_in , text.col = "grey", cex=1.2, pt.cex=3)

3.9 ggplot2

library(dplyr) #for dataset manipulation
library(reshape2)
library(knitr) #for neaty dataset printing
library(ggplot2)
data2 <- data[,58:82]
df <- melt(data2,id=c("Genotype","Treatment","Behavior","class..82" ))
ggplot(df, aes(x=variable,y=value,group=Genotype)) + 
  geom_point(aes(color=Genotype),size=0.5,shape=3)+
  scale_colour_brewer(palette = "Set1") +
  theme_economist() + 
  scale_color_economist() +
  theme(axis.text.x = element_text(angle=65, vjust=0.6))+
  theme(legend.background = element_rect( fill = 'grey95', size = 0.3, linetype='solid',colour ="darkblue"))+
  theme(legend.title = element_text(face="bold"))

#facet_wrap
df <- na.omit(df)
mean_df<-df %>% group_by(class..82,Genotype,variable) %>%
 summarise(mean=mean(value)) 

p <- ggplot(mean_df, aes(x=variable,y = mean,fill=class..82)) +
  geom_bar(position="dodge", stat="identity")+
  facet_wrap(~Genotype) +
  guides(fill=guide_legend("Treatment"))
p + 
  scale_fill_brewer(palette = "Spectral") + 
  coord_flip()

#facet_grid
df <- na.omit(df)
mean_df2 <-df %>% group_by(class..82,Behavior,variable) %>%
 summarise(mean=mean(value)) 

p1 <- ggplot(mean_df2, aes(x=variable,y = mean,fill=class..82)) +
  geom_bar(position="dodge", stat="identity")+
  facet_grid(Behavior~.) +
  guides(fill=guide_legend("Treatment"))
p1 + scale_fill_brewer(palette = "PuBu") +theme_dark() +theme(axis.text.x = element_text(angle=65, vjust=0.6)) 

#Multiple variables mean
#data1 <- na.omit(data)
#dfdf<-data1 %>% group_by(class..82) %>%
#  summarise_at(vars(Bcatenin_N:MTOR_N),mean, na.rm=TRUE)
#dfdf2<- melt(dfdf)
#kable(dfdf2)

3.10 Color

3.11 Plotly

#Linear
plot_ly(data=mean_df2, x=~variable,y=~mean,color=~class..82,type = 'scatter', mode = 'lines') 

#Scatter
plot_ly(data=data,x=~Tau_N, y=~ERK_N,
        marker = list(size = 5,
                       color = 'rgba(255, 182, 193, .9)',
                       line = list(color = 'rgba(152, 0, 0, .8)',
                                   width = 1))) %>%
  group_by(class..82) %>%
  layout(title = 'Styled Scatter')

x <- c(1:1080)
x1<- rep(1:180,6)
data2<- cbind(data,x1)

#smoothing line
ggplot(data2, aes(x=x1, y=SOD1_N,color=class..82)) + 
  geom_point(size=1) +
  geom_smooth(position = "identity")

#line + scatter
plot_ly(data2, x = ~x1, y = ~pNR2B_N, type = 'scatter',color=~class..82,mode = 'lines')

p <- plot_ly(data, x = ~x, y = ~pNR2B_N) 
add_markers(p, linetype = ~Behavior)

3.12 Lollipop

# Lillopop Plot
ggplot(mean_df2, aes(x=variable, y=mean)) + 
  geom_point(color="orange",size=4) + 
  geom_segment(aes(x=variable, xend=variable, y=0.5, yend=mean),color='grey') + 
  theme( panel.grid.major.x = element_blank(),
          panel.border = element_blank(),
          axis.ticks.x = element_blank(),
          axis.text.x = element_text(angle=65, vjust=0.6))+ 
  labs(title="Lollipop Chart", subtitle="Treatment") 

3.13 Density plot

theme_set(theme_classic())

g <- ggplot(data, aes(pGSK3B_Tyr216_N))
g + geom_density(aes(fill=factor(class..82)), alpha=0.8) + 
    labs(title="Density plot", 
         subtitle="pGSK3B_Tyr216_N Grouped by classs",
         caption="Source: ",
         x="pGSK3B_Tyr216_N",
         fill="#class") +
    facet_wrap(~Behavior)